Despite the overall improvement in health status, the timing and magnitude of changes in health outcomes (e.g. percentage of cancer late-stage diagnosis, adverse birth outcomes) display strong racial and geographical disparities. Quantifying the magnitude of these disparities at different scales and how they change with time are crucial metrics for understanding their origins and tracking progress towards their elimination. The proposed research will contribute to these important goals through: 1) the development of a geostatistical approach to identify and map nested scales of changes corresponding to individual -> neighborhood -> region, explore their relationships with covariates (e.g. other health outcomes or putative factors) through boundary overlap analysis and quantify the temporal stability of these spatial patterns, and 2) an in-depth boundary and multi-level analysis of the geographic and socioeconomic disparities in the incidence of breast cancer late-stage diagnosis and adverse birth outcomes (mortality and low birth weight), as well as their temporal changes, in Michigan. Specifically, this project will accomplish three aims: 1. Develop new methodologies for applying boundary analysis to raster data (e.g. imagery or disease risk maps) and within a multi-scale framework to account for the existence of boundaries at different spatial levels (e.g., counties, ZIP codes and census tracts), and implementing boundary analysis in a temporal framework to allow the study of temporal trends in the strength and location of geographic boundaries. 2. Explore the use of diffusion of innovation theory and simulated annealing-based spatial aggregation algorithms for the representation and exploratory data analysis of temporal trends in health outcomes and their relationship to putative factors in both space and time. 3. Apply the methodology to demonstrate the approach and its unique benefits for the investigation of geographical and racial disparities in temporal trends of several health outcomes (late-stage breast cancer, infant mortality and low birth weight), and the exploration of their relationships with potential factors, such as proximityto screening facilities (e.g. mammography clinics), socio-economic status, air pollution, and individual-level factors (e.g. smoking, health insurance, age). These technologic and scientific innovations will revolutionize our ability to visualize and interpret variation in cancer incidenceat multiple spatial scales and across time, which will help generating hypotheses for in depth individual studies of risk factors that are causal, or impact survival or morbidity, and establishig the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will also facilitate the long-term quantification of the benefits of current strategies and policies for reducing the observed geographic and racial disparities in cancer stage at diagnosis and incidence of infant mortality